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Found 9 papers, 5 papers with code
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption.
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning
In end-to-end autonomous driving, the utilization of existing sensor fusion techniques and navigational control methods for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents.
Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study
As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential.
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions
Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion.
Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation
Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification.
DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information
However, these cues can be misleading for objects with wide-range variation or adversarial situations, which is a challenging aspect of object-agnostic distance estimation.
Online Probabilistic Model Identification using Adaptive Recursive MCMC
We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which eliminates the shortcomings of conventional online techniques while computing the entire probability density function of model parameters.
ARMCMC: Online Bayesian Density Estimation of Model Parameters
Our method shows at-least 70\% improvement in parameter point estimation accuracy and approximately 55\% reduction in tracking error of the value of interest compared to recursive least squares and conventional MCMC.
ARMCMC: ONLINE MODEL PARAMETERS DENSITY ESTIMATION IN BAYESIAN PARADIGM
We demonstrate our approach on a challenging benchmark: estimation of parameters in the Hunt-Crossley dynamic model, which models both on/off contact forces applied to soft materials.
